This paper addresses the problem of corpus-level entity typing, i.e.,
inferring from a large corpus that an entity is a member of a class such as
"food" or "artist". The application of entity typing we are interested in is
knowledge base completion, specifically, to learn which classes an entity is a
member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-
based and combines (i) a global model that scores based on aggregated
contextual information of an entity and (ii) a context model that first scores
the individual occurrences of an entity and then aggregates the scores. Each of
the two proposed models has some specific properties. For the global model,
learning high quality entity representations is crucial because it is the only
source used for the predictions. Therefore, we introduce representations using
name and contexts of entities on the three levels of entity, word, and
character. We show each has complementary information and a multi-level
representation is the best. For the context model, we need to use distant
supervision since the context-level labels are not available for entities.
Distant supervised labels are noisy and this harms the performance of models.
Therefore, we introduce and apply new algorithms for noise mitigation using
multi-instance learning. We show the effectiveness of our models in a large
entity typing dataset, built from Freebase.

本文提出 FIGMENT 模型解决了基于语料库的实体类型问题。该模型使用多层次的实体表示和远程监督的噪声缓解算法，实现了知识库完成的任务。在基于 Freebase 的大型实体类型数据集中，该模型表现出很好的效果。

语料库级细粒度实体类型标注

Corpus-level Fine-grained Entity Typing

This paper addresses the problem of corpus-level entity typing, i.e.,
inferring from a large corpus that an entity is a member of a class such as
"food" or "artist". The application of entity typing we are interested in is
knowledge base completion, specifically, to learn which classes an entity is a
member of. We propose FIGMENT to tackle this problem. FIGMENT is
embedding-based and combines (i) a global model that scores based on aggregated
contextual information of an entity and (ii) a context model that first scores
the individual occurrences of an entity and then aggregates the scores. In our
evaluation, FIGMENT strongly outperforms an approach to entity typing that
relies on relations obtained by an open information extraction system.

本研究基于嵌入式方法，提出了 FIGMENT 来解决语料库实体类型划分问题，并在知识库完成中应用。FIGMENT 由全局模型和上下文模型组成，分别得分后进行综合评分，其性能优于依赖于开放信息提取系统获取关系的实体类型划分方法。